Novel microarray technologies such as the AB1700 platform from Applied Biosystems promise significant increases in the signal dynamic range and a higher sensitivity for weakly expressed transcripts. We have compared a representative set of AB1700 data with a similarly representative Affymetrix HG-U133A dataset. The AB1700 design extends the signal dynamic detection range at the lower bound by one order of magnitude. The lognormal signal distribution profiles of these high-sensitivity data need to be represented by two independent distributions. The additional second distribution covers those transcripts that would have gone undetected using the Affymetrix technology. The signal-dependent variance distribution in the AB1700 data is a non-trivial function of signal intensity, describable using a composite function. The drastically different structure of these high-sensitivity transcriptome profiles requires adaptation or even redevelopment of the standard microarray analysis methods. Based on the statistical properties, we have derived a signal variance distribution model for AB1700 data that is necessary for such development. Interestingly, the dual lognormal distribution observed in the AB1700 data reflects two fundamentally different biologic mechanisms of transcription initiation.
Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature. However, asymmetric gene expression changes are frequently observed, leading to suboptimal normalization results and in consequence potentially to thousands of false calls. Therefore, we have specifically investigated asymmetric comparative transcriptome profiles and developed the normalization using weighted negative second order exponential error functions (NeONORM) for robust and global inter-assay normalization. NeONORM efficiently damps true gene regulatory events in order to minimize their misleading impact on the normalization process. We evaluated NeONORM’s applicability using artificial and true experimental datasets, both of which demonstrated that NeONORM could be systematically applied to inter-assay and inter-condition comparisons.
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